Time Series Approach for Modelling the Merger and Acquisition Series: An Application to Indian Banking System

Year: 2021       Vol.: 70       No.: 1      

Authors: Varun Agiwal and Jitendra Kumar

Abstract:

In time series, present observation not only depend upon own past observation(s) but also involve other explanatory or exogenous variables. These variables are not continuously influence or impact for long run and may be removed or discontinued or merger and acquisition (M&A) because its effect may be reduced due to less significant correlation. The M&A theory is developed when one or more variables are not meet out the required circumstance to survive in the system. To analyze the performance of M&A concept, this study proposes a merged autoregressive (M-AR) model for examining the impact of merger into the parameters as well as acquired series. Bayesian approach is considered for parameter estimation under different loss functions and compared with least square estimator. To test the presence/association of merger series in the acquire series, Bayes factor, full Bayesian significance test and posterior probability based on credible interval are derived. A simulation study and an empirical application of banking indicators for Indian Banks are carried out to evaluate the performance of the proposed model. The study concludes that proposed time series models solved the problems of discontinuity in the series and also able to manage model statistically.

Keywords: autoregressive model, Bayesian inference, merger and acquisition series, Indian bank

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